1. Technical Field
This invention refers to methods and systems for optimizing the design of the surfaces of bodies moving through a fluid medium, and in particular methods and systems for optimizing the design of aircraft surfaces.
2. Description of the Related Art
Currently aircraft design is carried out using computational fluid dynamics (CFD) and wind tunnel testing (WTT) with the current trend being to reduce tunnel testing and increase simulation. The advantages of CFD simulation are significant, as the time needed to obtain a solution is reduced, and more optimized solutions may be obtained due to the flexibility and automation of the process.
A CFD calculation requires a 3D discrete model of the aircraft and its surrounding space and a CFD solver implemented in a computer. The discrete model of the aircraft is created using one or more computer programs to develop a volumetric grid where the geometry of the aircraft is divided into sub domains for the application of the surrounding conditions of the fluid dynamic problem. The CFD solver enables relevant CFD calculations to be defined for said discrete model.
Computational Fluid Dynamics (CFD) permits detailed calculations to be made of any system in which fluids are involved by means of solving basic equations for the conservation of matter, energy, and amount of movement of the specific geometry of each system considered. The results obtained are the values of all the variables which characterize the flow field (speed, pressure, temperature, composition, etc) in each of the points thereof.
In this respect the simulation methods known in the art and used to optimize design of aerodynamic surfaces follow the stages of the diagram represented in FIG. 1.
In the first stage 11 the initial geometry of the surface in question is defined, generally using CAD based on 2D plans or drawings which contain the basic characteristics of the design.
In the second stage 13 a computational grid is generated. The domain in question thus becomes discrete divided into small cells with different forms. The complexity of the physics involved, together with the size of the domain largely defines the size of the problem and the calculation power needed. The node density may change from some regions to others requiring accumulation of a greater number of these in zones where considerable variations of a variable are expected.
In the third stage 15 equations are solved, governing the variables of interest for the design of the surface of each of the elements of the computational grid generated in the previous stage. Since the equations are in partial derivatives, it is necessary to convert them to algebraic equations (introducing numerical discretization errors and truncation) using the most appropriate numerical schemes. Thus a group of equations in partial derivatives on a continuous space (x, y, z, t) becomes a finite system of algebraic equations with independent discrete variables (x[i],y[i], z[i],t[j].
In the fourth stage 17 the results obtained are analyzed and if the distribution of values of the objective functions is not satisfactory, an iterative cycle is created, the first step 19 of which is to modify the computational grid and subsequently repeat the third and fourth stages 15, 17, in order to make the CFD calculations and analyze their results in relation to the grid modified in the step 19. Having obtained good results, the final stage 21 is undertaken in which the geometric definition of the “optimized” surface is obtained based on the computational grid.
As may be deduced from the foregoing, in this design process there is no link between the geometric analysis and the simulation analysis. The process is based on a geometric definition and ends with a modified geometry, however the modification is not the result of a geometric analysis but a simulation analysis. This leads to greater cost and duration of the design processes.
This invention is designed to overcome this disadvantage.